Tumor evolution project

Data used

In this notebook, we are using the tmb_genomic.tsv file generated from the 01-preprocess-data.Rmd script.

Set up

suppressPackageStartupMessages({
  library(tidyverse)
  library(scales)
})

Directories and File Inputs/Outputs

# Detect the ".git" folder. This will be in the project root directory.
# Use this as the root directory to ensure proper sourcing of functions
# no matter where this is called from.
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "analyses", "tmb-vaf-longitudinal") 
results_dir <- file.path(analysis_dir, "results")

# Input files
tmb_genomic_file <- file.path(results_dir, "tmb_vaf_genomic.tsv")
palette_file <- file.path(root_dir, "figures", "palettes", "tumor_descriptor_color_palette.tsv")

# File path to plots directory
plots_dir <-
  file.path(analysis_dir, "plots")
if (!dir.exists(plots_dir)) {
  dir.create(plots_dir)
}

# File path to dumbbell plots directory
dumbbell_plots_dir <-
  file.path(plots_dir, "dumbbell")
if (!dir.exists(dumbbell_plots_dir )) {
  dir.create(dumbbell_plots_dir )
}



source(paste0(analysis_dir, "/util/function-create-barplot.R"))
source(paste0(analysis_dir, "/util/function-create-dumbbell-plot.R"))
source(paste0(root_dir, "/figures/scripts/theme.R"))

Read in data and process

# Read and process tmb_genomic file
df_total <- readr::read_tsv(tmb_genomic_file, guess_max = 100000, show_col_types = FALSE) %>% 
  group_by(Kids_First_Participant_ID) %>% 
  mutate(cg_distinct = n_distinct(cancer_group) > 1) # to identify samples with different diagnosis across timepoints

# Are there any samples with both WGS and WXS? 
df_total %>% 
  unique() %>% 
  arrange(Kids_First_Participant_ID, experimental_strategy) %>%
  group_by(Kids_First_Participant_ID) %>%
  dplyr::summarise(experimental_strategy_sum = str_c(experimental_strategy, collapse = ";")) 
# There are, so let's remove these from downstream analyses.
df <- df_total %>% 
  filter(!experimental_strategy == "WXS") %>% 
  dplyr::mutate(patient_id = paste(short_histology, Kids_First_Participant_ID, sep = "_")) %>% 
  distinct(cancer_group, .keep_all = TRUE) %>% 
  summarise(cg_sum = str_c(cancer_group, collapse = ",")) %>% # to identify cases with multiple diagnosis
  left_join(df_total, by = c("Kids_First_Participant_ID")) %>% 
  select(Kids_First_Participant_ID, Kids_First_Biospecimen_ID, cg_sum, cancer_group, short_histology, tumor_descriptor, descriptors, tumor_descriptor_sum, tmb, mutation_count)

# How many bs_samples per cg_sum?
print(df %>% count(cg_sum) %>% arrange(desc(n))) 
# A tibble: 41 × 2
   cg_sum                               n
   <chr>                            <int>
 1 High-grade glioma                26273
 2 Meningioma,Medulloblastoma        3713
 3 Atypical Teratoid Rhabdoid Tumor  2945
 4 Rosai-Dorfman disease,Sarcoma     2819
 5 Diffuse midline glioma            2397
 6 Medulloblastoma                   1224
 7 Ependymoma                         947
 8 Low-grade glioma                   781
 9 Chordoma                           477
10 Meningioma                         223
# ℹ 31 more rows
# Let's summarize cancer groups with < 10 bs_samples as Other and use this for visualization purposes
cg_sum_df <- df %>% 
  count(cg_sum) %>% 
  dplyr::mutate(cg_sum_n = glue::glue("{cg_sum} (N={n})"))

df <- df %>% 
  left_join(cg_sum_df, by = c("cg_sum")) %>% 
  mutate(cg_plot = case_when(n < 10 ~ "Other",
                             TRUE ~ cg_sum),
         cg_kids_id = paste(cg_sum, Kids_First_Participant_ID, sep = "_"))

# How many bs_samples per cg_plot?
print(df %>% count(cg_plot) %>% arrange(desc(n))) 
# A tibble: 41 × 2
   cg_plot                              n
   <chr>                            <int>
 1 High-grade glioma                26273
 2 Meningioma,Medulloblastoma        3713
 3 Atypical Teratoid Rhabdoid Tumor  2945
 4 Rosai-Dorfman disease,Sarcoma     2819
 5 Diffuse midline glioma            2397
 6 Medulloblastoma                   1224
 7 Ependymoma                         947
 8 Low-grade glioma                   781
 9 Chordoma                           477
10 Meningioma                         223
# ℹ 31 more rows
# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE)

# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$color_names

# length(unique(df$Kids_First_Participant_ID))

TMB per Patient case

We will explore TMB per Kids_First_Participant_ID over time by creating stacked barplots.

# Define parameters for function
ylim = max(df$tmb)
x_value <- df$Kids_First_Participant_ID

# Re-order df
f <- c("Second Malignancy", "Unavailable", "Deceased", "Recurrence", "Progressive", "Diagnosis") # Level df by timepoints
df_plot <- df %>% 
  dplyr:::mutate(tumor_descriptor = factor(tumor_descriptor),
                 tumor_descriptor = fct_relevel(tumor_descriptor, f)) 
Warning: There was 1 warning in `dplyr:::mutate()`.
ℹ In argument: `tumor_descriptor = fct_relevel(tumor_descriptor, f)`.
Caused by warning:
! 1 unknown level in `f`: Unavailable
# Run function
fname <- paste0(plots_dir, "/", "TMB-genomic-total.pdf")
print(fname)
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/TMB-genomic-total.pdf"
p <- create_stacked_barplot(tmb_df = df_plot, ylim = ylim, x = x_value, palette = palette)
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
Warning: Removed 34728 rows containing missing values (`geom_col()`).
Warning: Removed 34770 rows containing missing values (`geom_bar()`).

pdf(file = fname, width = 22, height = 6)
print(p)
Warning: Removed 34728 rows containing missing values (`geom_col()`).
Removed 34770 rows containing missing values (`geom_bar()`).
dev.off()
png 
  2 

Attention: Hypermutant TMB defined as ≥10 Mb, and Ultrahypermutant TMB defined as ≥100 mutations/Mb in pediatric brain tumors (https://pubmed.ncbi.nlm.nih.gov/29056344/).

Here, we notice that there are samples with high TMB (hyper-mutant samples). Next, we will exclude these samples (threshold >= 10) from downstream analysis. Attention is needed in cases with high number of mutations in only one timepoint as this will lead to un-matched longitudinal samples. We will also remove those so we always have matched longitudinal samples.

# Filter df and remove any samples with single timepoints
df_plot_filter <- df %>%
  filter(!tmb >= 10) %>%
  unique() %>% 
  arrange(Kids_First_Participant_ID, tumor_descriptor) %>%
  group_by(Kids_First_Participant_ID) %>%
  dplyr::summarise(tumor_descriptor_sum2 = str_c(tumor_descriptor, collapse = ";")) %>% 
  dplyr::filter(!tumor_descriptor_sum2 %in% c("Diagnosis", "Progressive", "Recurrence", "Second Malignancy", "Unavailable", "Deceased", "Diagnosis;Diagnosis","Progressive;Progressive")) %>% 
  left_join(df, by = c("Kids_First_Participant_ID")) %>% 
  drop_na(tmb) %>% 
  mutate(cg_plot = str_replace(cg_plot, c("/"), " "),
         tumor_descriptor = factor(tumor_descriptor),
         tumor_descriptor = fct_relevel(tumor_descriptor, f)) %>% 
  arrange(tumor_descriptor) 
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `tumor_descriptor = fct_relevel(tumor_descriptor, f)`.
Caused by warning:
! 1 unknown level in `f`: Unavailable
# length(unique(df_plot_filter$Kids_First_Participant_ID))


# Define parameters for function
ylim <- max(df_plot_filter$tmb)
df_plot_filter <- df_plot_filter
x_value <- df_plot_filter$cg_kids_id

# Run function
fname <- paste0(plots_dir, "/", "TMB-genomic-no-hypermutants.pdf")
print(fname)
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/TMB-genomic-no-hypermutants.pdf"
p <- create_stacked_barplot(tmb_df = df_plot_filter, ylim = ylim, x = x_value, palette = palette)
Warning: Removed 7163 rows containing missing values (`geom_col()`).
Warning: Removed 6923 rows containing missing values (`geom_bar()`).

pdf(file = fname, width = 25, height = 10)
print(p)
Warning: Removed 7163 rows containing missing values (`geom_col()`).
Removed 6923 rows containing missing values (`geom_bar()`).
dev.off()
png 
  2 

TMB across timepoints and cancer types per Patient case

We will explore TMB per cancer group over time by creating dumbbell plots. We classified by using cancer types with the highest number of samples (High- and Low-grade gliomas) versus any other cancer groups.

# How many bs_samples per kids_id and cancer group?
# print(table(df_plot_filter$cg_plot))
print(df_plot_filter %>% 
        count(cg_plot, Kids_First_Participant_ID))
# A tibble: 121 × 3
   cg_plot                                          Kids_First_Participa…¹     n
   <chr>                                            <chr>                  <int>
 1 Adamantinomatous Craniopharyngioma               PT_CBTW4E3X                9
 2 Adamantinomatous Craniopharyngioma               PT_WYXTEG3E               23
 3 Adamantinomatous Craniopharyngioma               PT_YK7AD0KK               20
 4 Adamantinomatous Craniopharyngioma,Craniopharyn… PT_WWZWD4KC               10
 5 Atypical Teratoid Rhabdoid Tumor                 PT_0WQFCZ6S               62
 6 Atypical Teratoid Rhabdoid Tumor                 PT_3KM9W8S8              115
 7 Atypical Teratoid Rhabdoid Tumor                 PT_6N825561             2224
 8 Atypical Teratoid Rhabdoid Tumor                 PT_DVXE38EX              198
 9 Atypical Teratoid Rhabdoid Tumor                 PT_ESHACWF6              159
10 Atypical Teratoid Rhabdoid Tumor                 PT_HE8FBFNA               16
# ℹ 111 more rows
# ℹ abbreviated name: ¹​Kids_First_Participant_ID
# Dumbbell plot per cancer group
cancer_groups <- unique(as.character(df_plot_filter$cg_plot))
cancer_groups <- sort(cancer_groups, decreasing = FALSE)
print(cancer_groups)
 [1] "Adamantinomatous Craniopharyngioma"                            
 [2] "Adamantinomatous Craniopharyngioma,Craniopharyngioma"          
 [3] "Atypical Teratoid Rhabdoid Tumor"                              
 [4] "Chordoma"                                                      
 [5] "Choroid plexus tumor"                                          
 [6] "CNS Embryonal tumor"                                           
 [7] "Diffuse leptomeningeal glioneuronal tumor,Low-grade glioma"    
 [8] "Diffuse midline glioma"                                        
 [9] "Dysembryoplastic neuroepithelial tumor"                        
[10] "Dysembryoplastic neuroepithelial tumor,Ganglioglioma"          
[11] "Embryonal tumor with multilayer rosettes"                      
[12] "Ependymoma"                                                    
[13] "Ependymoma,Dysembryoplastic neuroepithelial tumor"             
[14] "Ependymoma,Other tumor"                                        
[15] "Ewing sarcoma"                                                 
[16] "Ganglioglioma"                                                 
[17] "Glial-neuronal tumor NOS,Ganglioglioma"                        
[18] "Hemangioblastoma"                                              
[19] "High-grade glioma"                                             
[20] "High-grade glioma,Low-grade glioma"                            
[21] "Low-grade glioma"                                              
[22] "Low-grade glioma,Glial-neuronal tumor NOS"                     
[23] "Low-grade glioma,High-grade glioma"                            
[24] "Low-grade glioma,Schwannoma,Neurofibroma Plexiform"            
[25] "Medulloblastoma"                                               
[26] "Medulloblastoma,Meningioma"                                    
[27] "Meningioma"                                                    
[28] "Meningioma,Adamantinomatous Craniopharyngioma"                 
[29] "Neuroblastoma,Ganglioneuroblastoma"                            
[30] "Neurofibroma Plexiform"                                        
[31] "Neurofibroma Plexiform,Malignant peripheral nerve sheath tumor"
[32] "Other tumor,Medulloblastoma"                                   
[33] "Pilocytic astrocytoma"                                         
[34] "Pilocytic astrocytoma,Low-grade glioma"                        
[35] "Pineoblastoma,Fibromyxoid lesion"                              
[36] "Schwannoma"                                                    
for (i in seq_along(cancer_groups)) {
  print(i)
  df_ct_sub <- df_plot_filter %>% 
    filter(cg_plot == cancer_groups [i])
  
      if (i %in% c(3, 7, 8)) {
    print(cancer_groups [i])
    # Define parameters for function
    ylim <- 2
    } else if (i == 2) {
      print(cancer_groups [i])
      # Define parameters for function
      ylim <- 6
      } else {
        print(cancer_groups [i])
        # Define parameters for function
        ylim <- 4
      }
    

    # Name plots
    fname <- paste0(dumbbell_plots_dir, "/", cancer_groups[i], "-TMB-dumbbell", ".pdf")
    print(fname)
    
    # Run function
    p <- create_dumbbell_ct(tmb_df = df_ct_sub, 
                                 ylim = ylim, 
                                 ct_id = cancer_groups[i],
                                 palette = palette)
    pdf(file = fname, width = 12, height = 8)
    print(p)
    dev.off()
}
[1] 1
[1] "Adamantinomatous Craniopharyngioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Adamantinomatous Craniopharyngioma-TMB-dumbbell.pdf"

[1] 2
[1] "Adamantinomatous Craniopharyngioma,Craniopharyngioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Adamantinomatous Craniopharyngioma,Craniopharyngioma-TMB-dumbbell.pdf"

[1] 3
[1] "Atypical Teratoid Rhabdoid Tumor"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Atypical Teratoid Rhabdoid Tumor-TMB-dumbbell.pdf"
Warning: Removed 2235 rows containing missing values (`geom_line()`).
Warning: Removed 2410 rows containing missing values (`geom_point()`).
Warning: Removed 2235 rows containing missing values (`geom_line()`).
Warning: Removed 2410 rows containing missing values (`geom_point()`).

[1] 4
[1] "Chordoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Chordoma-TMB-dumbbell.pdf"

[1] 5
[1] "Choroid plexus tumor"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Choroid plexus tumor-TMB-dumbbell.pdf"

[1] 6
[1] "CNS Embryonal tumor"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/CNS Embryonal tumor-TMB-dumbbell.pdf"

[1] 7
[1] "Diffuse leptomeningeal glioneuronal tumor,Low-grade glioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Diffuse leptomeningeal glioneuronal tumor,Low-grade glioma-TMB-dumbbell.pdf"

[1] 8
[1] "Diffuse midline glioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Diffuse midline glioma-TMB-dumbbell.pdf"
Warning: Removed 647 rows containing missing values (`geom_line()`).
Warning: Removed 647 rows containing missing values (`geom_point()`).
Warning: Removed 647 rows containing missing values (`geom_line()`).
Warning: Removed 647 rows containing missing values (`geom_point()`).

[1] 9
[1] "Dysembryoplastic neuroepithelial tumor"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Dysembryoplastic neuroepithelial tumor-TMB-dumbbell.pdf"

[1] 10
[1] "Dysembryoplastic neuroepithelial tumor,Ganglioglioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Dysembryoplastic neuroepithelial tumor,Ganglioglioma-TMB-dumbbell.pdf"

[1] 11
[1] "Embryonal tumor with multilayer rosettes"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Embryonal tumor with multilayer rosettes-TMB-dumbbell.pdf"

[1] 12
[1] "Ependymoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Ependymoma-TMB-dumbbell.pdf"

[1] 13
[1] "Ependymoma,Dysembryoplastic neuroepithelial tumor"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Ependymoma,Dysembryoplastic neuroepithelial tumor-TMB-dumbbell.pdf"

[1] 14
[1] "Ependymoma,Other tumor"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Ependymoma,Other tumor-TMB-dumbbell.pdf"

[1] 15
[1] "Ewing sarcoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Ewing sarcoma-TMB-dumbbell.pdf"

[1] 16
[1] "Ganglioglioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Ganglioglioma-TMB-dumbbell.pdf"

[1] 17
[1] "Glial-neuronal tumor NOS,Ganglioglioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Glial-neuronal tumor NOS,Ganglioglioma-TMB-dumbbell.pdf"

[1] 18
[1] "Hemangioblastoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Hemangioblastoma-TMB-dumbbell.pdf"

[1] 19
[1] "High-grade glioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/High-grade glioma-TMB-dumbbell.pdf"

[1] 20
[1] "High-grade glioma,Low-grade glioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/High-grade glioma,Low-grade glioma-TMB-dumbbell.pdf"

[1] 21
[1] "Low-grade glioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Low-grade glioma-TMB-dumbbell.pdf"

[1] 22
[1] "Low-grade glioma,Glial-neuronal tumor NOS"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Low-grade glioma,Glial-neuronal tumor NOS-TMB-dumbbell.pdf"

[1] 23
[1] "Low-grade glioma,High-grade glioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Low-grade glioma,High-grade glioma-TMB-dumbbell.pdf"

[1] 24
[1] "Low-grade glioma,Schwannoma,Neurofibroma Plexiform"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Low-grade glioma,Schwannoma,Neurofibroma Plexiform-TMB-dumbbell.pdf"

[1] 25
[1] "Medulloblastoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Medulloblastoma-TMB-dumbbell.pdf"

[1] 26
[1] "Medulloblastoma,Meningioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Medulloblastoma,Meningioma-TMB-dumbbell.pdf"

[1] 27
[1] "Meningioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Meningioma-TMB-dumbbell.pdf"

[1] 28
[1] "Meningioma,Adamantinomatous Craniopharyngioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Meningioma,Adamantinomatous Craniopharyngioma-TMB-dumbbell.pdf"

[1] 29
[1] "Neuroblastoma,Ganglioneuroblastoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Neuroblastoma,Ganglioneuroblastoma-TMB-dumbbell.pdf"

[1] 30
[1] "Neurofibroma Plexiform"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Neurofibroma Plexiform-TMB-dumbbell.pdf"

[1] 31
[1] "Neurofibroma Plexiform,Malignant peripheral nerve sheath tumor"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Neurofibroma Plexiform,Malignant peripheral nerve sheath tumor-TMB-dumbbell.pdf"

[1] 32
[1] "Other tumor,Medulloblastoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Other tumor,Medulloblastoma-TMB-dumbbell.pdf"

[1] 33
[1] "Pilocytic astrocytoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Pilocytic astrocytoma-TMB-dumbbell.pdf"

[1] 34
[1] "Pilocytic astrocytoma,Low-grade glioma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Pilocytic astrocytoma,Low-grade glioma-TMB-dumbbell.pdf"

[1] 35
[1] "Pineoblastoma,Fibromyxoid lesion"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Pineoblastoma,Fibromyxoid lesion-TMB-dumbbell.pdf"

[1] 36
[1] "Schwannoma"
[1] "/home/rstudio/pbta-tumor-evolution/analyses/tmb-vaf-longitudinal/plots/dumbbell/Schwannoma-TMB-dumbbell.pdf"

Total number of mutations across timepoints and biospecimen sample per Patient case

Here, we want to explore the number of mutations per timepoint and biospecimen sample per patient case.

samples <- unique(as.character(df_plot_filter$Kids_First_Participant_ID))
print(samples)
  [1] "PT_2ECVKTTQ" "PT_82MX6J77" "PT_98QMQZY7" "PT_9PJR0ZK7" "PT_T2M1338J"
  [6] "PT_WWRB8KDQ" "PT_19GCSK2S" "PT_1H2REHT2" "PT_23NZGSRJ" "PT_37B5JRP1"
 [11] "PT_394ZA6P7" "PT_3AR6AW9N" "PT_3KM9W8S8" "PT_5NS35B66" "PT_6N825561"
 [16] "PT_75HRTX4S" "PT_8GN3TQRM" "PT_CWVNNBPH" "PT_CXT81GRM" "PT_DNAJYFZT"
 [21] "PT_DR94DMTG" "PT_DVXE38EX" "PT_FA2F3HQG" "PT_FN57KS79" "PT_FWWRWTV2"
 [26] "PT_GTHZF21E" "PT_HFQNKP5X" "PT_HJMP6PH2" "PT_KBFM551M" "PT_KTRJ8TFY"
 [31] "PT_KZ56XHJT" "PT_MDWPRDBT" "PT_MNSEJCDM" "PT_NJQ26FHN" "PT_NZ85YSJ1"
 [36] "PT_PR4YBBH3" "PT_RJ1TJ2KH" "PT_SD4RJ57T" "PT_WYXTEG3E" "PT_YND59052"
 [41] "PT_Z4BF2NSB" "PT_00G007DM" "PT_0DWRY9ZX" "PT_0WQFCZ6S" "PT_2MZPGZN1"
 [46] "PT_2YT37G8P" "PT_3T3VGWC6" "PT_7M2PGCBV" "PT_89XRZBSG" "PT_962TCBVR"
 [51] "PT_99S5BPE3" "PT_BRVGRXQY" "PT_BZCJMEX8" "PT_C1RDBCVM" "PT_CBTW4E3X"
 [56] "PT_CWXSP19D" "PT_D6AJHDST" "PT_DFQAH7RS" "PT_EQX0VT4F" "PT_HHG37M6W"
 [61] "PT_HXV713W6" "PT_JP1FDKN9" "PT_KMHGNCNR" "PT_N8W26H19" "PT_NPETR8RY"
 [66] "PT_PF04R0BH" "PT_PFA762TK" "PT_QH9H491G" "PT_S2SQJVGK" "PT_T4VN7ZRB"
 [71] "PT_TP6GS00H" "PT_W6AWJJK7" "PT_XZGWKXC5" "PT_YK7AD0KK" "PT_Z4GS3ZQQ"
 [76] "PT_ZZRBX5JT" "PT_02J5CWN5" "PT_04V47WFC" "PT_1ZAWNGWT" "PT_25Z2NX27"
 [81] "PT_2FVTD0WR" "PT_39H4JN6H" "PT_3GYW6P6P" "PT_3P3HARZ2" "PT_3R0P995B"
 [86] "PT_3VCS1PPF" "PT_5CYJ3NZ9" "PT_5ZPPR06P" "PT_62G82T6Q" "PT_6S1TFJ3D"
 [91] "PT_773ZPTEB" "PT_7WYPEC3Q" "PT_9S6WMQ92" "PT_AQWDQW27" "PT_AV0W0V8D"
 [96] "PT_B5DQ8FF0" "PT_ESHACWF6" "PT_FN4GEEFR" "PT_HE8FBFNA" "PT_JNEV57VK"
[101] "PT_JSFBMK5V" "PT_K8ZV7APT" "PT_NK8A49X5" "PT_P571HTNK" "PT_PAPEQ0T0"
[106] "PT_QEP13FH4" "PT_QJDY4Y9P" "PT_S4YNE17X" "PT_TKWTTRQ7" "PT_TRZ1N1HQ"
[111] "PT_VTG1S395" "PT_WP871F5S" "PT_WWZWD4KC" "PT_XA98HG1C" "PT_XTVQB9S4"
[116] "PT_Y98Q8XKV" "PT_YGN06RPZ" "PT_ZMKMKCFQ" "PT_XHYBZKCX" "PT_CBAYDRF9"
[121] "PT_PRJMB0SZ"
for (i in seq_along(samples)) {
  print(i)
  tmb_sub <- df_plot_filter %>%
    filter(Kids_First_Participant_ID == samples[i])
  
  # Define parameters for function
  ylim = max(df_plot_filter$mutation_count)
 
  # Run function
  p <- create_barplot_sample(tmb_df = tmb_sub,
                             ylim = ylim,
                             sid = samples[i],
                             palette = palette)
  print(p)
}
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sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggthemes_4.2.4  scales_1.2.1    lubridate_1.9.2 forcats_1.0.0  
 [5] stringr_1.5.0   dplyr_1.1.1     purrr_1.0.1     readr_2.1.4    
 [9] tidyr_1.3.0     tibble_3.2.1    ggplot2_3.4.0   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] highr_0.10       bslib_0.4.2      compiler_4.2.3   pillar_1.9.0    
 [5] jquerylib_0.1.4  tools_4.2.3      bit_4.0.5        digest_0.6.31   
 [9] timechange_0.2.0 jsonlite_1.8.4   evaluate_0.20    lifecycle_1.0.3 
[13] gtable_0.3.3     pkgconfig_2.0.3  rlang_1.1.0      cli_3.6.1       
[17] parallel_4.2.3   yaml_2.3.7       xfun_0.38        fastmap_1.1.1   
[21] withr_2.5.0      knitr_1.42       generics_0.1.3   vctrs_0.6.2     
[25] sass_0.4.5       hms_1.1.3        bit64_4.0.5      rprojroot_2.0.3 
[29] tidyselect_1.2.0 glue_1.6.2       R6_2.5.1         fansi_1.0.4     
[33] vroom_1.6.1      rmarkdown_2.21   farver_2.1.1     tzdb_0.3.0      
[37] magrittr_2.0.3   htmltools_0.5.5  colorspace_2.1-0 labeling_0.4.2  
[41] utf8_1.2.3       stringi_1.7.12   munsell_0.5.0    cachem_1.0.7    
[45] crayon_1.5.2    
---
title: "Explore TMB and number of mutations across multiple timepoints of the PBTA Cohort"
author: "Antonia Chroni <chronia@chop.edu> for D3B"
date: "`r Sys.Date()`"
output:
  html_notebook:
    toc: TRUE
    toc_float: TRUE
---

#### Tumor evolution project 

### Data used 
In this notebook, we are using the `tmb_genomic.tsv` file generated from the `01-preprocess-data.Rmd` script.

# Set up
```{r load-library}
suppressPackageStartupMessages({
  library(tidyverse)
  library(scales)
})
```

# Directories and File Inputs/Outputs
```{r set-dir-and-file-names}
# Detect the ".git" folder. This will be in the project root directory.
# Use this as the root directory to ensure proper sourcing of functions
# no matter where this is called from.
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "analyses", "tmb-vaf-longitudinal") 
results_dir <- file.path(analysis_dir, "results")

# Input files
tmb_genomic_file <- file.path(results_dir, "tmb_vaf_genomic.tsv")
palette_file <- file.path(root_dir, "figures", "palettes", "tumor_descriptor_color_palette.tsv")

# File path to plots directory
plots_dir <-
  file.path(analysis_dir, "plots")
if (!dir.exists(plots_dir)) {
  dir.create(plots_dir)
}

# File path to dumbbell plots directory
dumbbell_plots_dir <-
  file.path(plots_dir, "dumbbell")
if (!dir.exists(dumbbell_plots_dir )) {
  dir.create(dumbbell_plots_dir )
}



source(paste0(analysis_dir, "/util/function-create-barplot.R"))
source(paste0(analysis_dir, "/util/function-create-dumbbell-plot.R"))
source(paste0(root_dir, "/figures/scripts/theme.R"))
```

# Read in data and process
```{r read_input_files}
# Read and process tmb_genomic file
df_total <- readr::read_tsv(tmb_genomic_file, guess_max = 100000, show_col_types = FALSE) %>% 
  group_by(Kids_First_Participant_ID) %>% 
  mutate(cg_distinct = n_distinct(cancer_group) > 1) # to identify samples with different diagnosis across timepoints

# Are there any samples with both WGS and WXS? 
df_total %>% 
  unique() %>% 
  arrange(Kids_First_Participant_ID, experimental_strategy) %>%
  group_by(Kids_First_Participant_ID) %>%
  dplyr::summarise(experimental_strategy_sum = str_c(experimental_strategy, collapse = ";")) 

# There are, so let's remove these from downstream analyses.
df <- df_total %>% 
  filter(!experimental_strategy == "WXS") %>% 
  dplyr::mutate(patient_id = paste(short_histology, Kids_First_Participant_ID, sep = "_")) %>% 
  distinct(cancer_group, .keep_all = TRUE) %>% 
  summarise(cg_sum = str_c(cancer_group, collapse = ",")) %>% # to identify cases with multiple diagnosis
  left_join(df_total, by = c("Kids_First_Participant_ID")) %>% 
  select(Kids_First_Participant_ID, Kids_First_Biospecimen_ID, cg_sum, cancer_group, short_histology, tumor_descriptor, descriptors, tumor_descriptor_sum, tmb, mutation_count)

# How many bs_samples per cg_sum?
print(df %>% count(cg_sum) %>% arrange(desc(n))) 

# Let's summarize cancer groups with < 10 bs_samples as Other and use this for visualization purposes
cg_sum_df <- df %>% 
  count(cg_sum) %>% 
  dplyr::mutate(cg_sum_n = glue::glue("{cg_sum} (N={n})"))

df <- df %>% 
  left_join(cg_sum_df, by = c("cg_sum")) %>% 
  mutate(cg_plot = case_when(n < 10 ~ "Other",
                             TRUE ~ cg_sum),
         cg_kids_id = paste(cg_sum, Kids_First_Participant_ID, sep = "_"))

# How many bs_samples per cg_plot?
print(df %>% count(cg_plot) %>% arrange(desc(n))) 

# Read color palette
palette_df <- readr::read_tsv(palette_file, guess_max = 100000, show_col_types = FALSE)

# Define and order palette
palette <- palette_df$hex_codes
names(palette) <- palette_df$color_names

# length(unique(df$Kids_First_Participant_ID))
```

# TMB per Patient case
We will explore TMB per `Kids_First_Participant_ID` over time by creating stacked barplots.

```{r create-stacked-barplot, fig.width = 22, fig.height = 6, fig.fullwidth = TRUE}
# Define parameters for function
ylim = max(df$tmb)
x_value <- df$Kids_First_Participant_ID

# Re-order df
f <- c("Second Malignancy", "Unavailable", "Deceased", "Recurrence", "Progressive", "Diagnosis") # Level df by timepoints
df_plot <- df %>% 
  dplyr:::mutate(tumor_descriptor = factor(tumor_descriptor),
                 tumor_descriptor = fct_relevel(tumor_descriptor, f)) 

# Run function
fname <- paste0(plots_dir, "/", "TMB-genomic-total.pdf")
print(fname)
p <- create_stacked_barplot(tmb_df = df_plot, ylim = ylim, x = x_value, palette = palette)
pdf(file = fname, width = 22, height = 6)
print(p)
dev.off()
```
Attention: Hypermutant TMB defined as ≥10 Mb, and Ultrahypermutant TMB defined as ≥100 mutations/Mb in pediatric brain tumors (https://pubmed.ncbi.nlm.nih.gov/29056344/).

Here, we notice that there are samples with high TMB (hyper-mutant samples). Next, we will exclude these samples (threshold >= 10) from downstream analysis. Attention is needed in cases with high number of mutations in only one timepoint as this will lead to un-matched longitudinal samples. We will also remove those so we always have matched longitudinal samples.

```{r create-stacked-barplot-filter, fig.width = 25, fig.height = 10, fig.fullwidth = TRUE}
# Filter df and remove any samples with single timepoints
df_plot_filter <- df %>%
  filter(!tmb >= 10) %>%
  unique() %>% 
  arrange(Kids_First_Participant_ID, tumor_descriptor) %>%
  group_by(Kids_First_Participant_ID) %>%
  dplyr::summarise(tumor_descriptor_sum2 = str_c(tumor_descriptor, collapse = ";")) %>% 
  dplyr::filter(!tumor_descriptor_sum2 %in% c("Diagnosis", "Progressive", "Recurrence", "Second Malignancy", "Unavailable", "Deceased", "Diagnosis;Diagnosis","Progressive;Progressive")) %>% 
  left_join(df, by = c("Kids_First_Participant_ID")) %>% 
  drop_na(tmb) %>% 
  mutate(cg_plot = str_replace(cg_plot, c("/"), " "),
         tumor_descriptor = factor(tumor_descriptor),
         tumor_descriptor = fct_relevel(tumor_descriptor, f)) %>% 
  arrange(tumor_descriptor) 

# length(unique(df_plot_filter$Kids_First_Participant_ID))


# Define parameters for function
ylim <- max(df_plot_filter$tmb)
df_plot_filter <- df_plot_filter
x_value <- df_plot_filter$cg_kids_id

# Run function
fname <- paste0(plots_dir, "/", "TMB-genomic-no-hypermutants.pdf")
print(fname)
p <- create_stacked_barplot(tmb_df = df_plot_filter, ylim = ylim, x = x_value, palette = palette)
pdf(file = fname, width = 25, height = 10)
print(p)
dev.off()
```

# TMB across timepoints and cancer types per Patient case
We will explore TMB per cancer group over time by creating dumbbell plots. We classified by using cancer types with the highest number of samples (High- and Low-grade gliomas) versus any other cancer groups.

```{r create-dumbbell-ct, fig.width = 12, fig.height = 8, fig.fullwidth = TRUE}
# How many bs_samples per kids_id and cancer group?
# print(table(df_plot_filter$cg_plot))
print(df_plot_filter %>% 
        count(cg_plot, Kids_First_Participant_ID))
       
# Dumbbell plot per cancer group
cancer_groups <- unique(as.character(df_plot_filter$cg_plot))
cancer_groups <- sort(cancer_groups, decreasing = FALSE)
print(cancer_groups)

for (i in seq_along(cancer_groups)) {
  print(i)
  df_ct_sub <- df_plot_filter %>% 
    filter(cg_plot == cancer_groups [i])
  
      if (i %in% c(3, 7, 8)) {
    print(cancer_groups [i])
    # Define parameters for function
    ylim <- 2
    } else if (i == 2) {
      print(cancer_groups [i])
      # Define parameters for function
      ylim <- 6
      } else {
        print(cancer_groups [i])
        # Define parameters for function
        ylim <- 4
      }
    

    # Name plots
    fname <- paste0(dumbbell_plots_dir, "/", cancer_groups[i], "-TMB-dumbbell", ".pdf")
    print(fname)
    
    # Run function
    p <- create_dumbbell_ct(tmb_df = df_ct_sub, 
                                 ylim = ylim, 
                                 ct_id = cancer_groups[i],
                                 palette = palette)
    pdf(file = fname, width = 12, height = 8)
    print(p)
    dev.off()
}
```

# Total number of mutations across timepoints and biospecimen sample per Patient case
Here, we want to explore the number of mutations per timepoint and biospecimen sample per patient case.

```{r create-barplot-sample, fig.width = 5, fig.height = 4, fig.fullwidth = TRUE}
samples <- unique(as.character(df_plot_filter$Kids_First_Participant_ID))
print(samples)

for (i in seq_along(samples)) {
  print(i)
  tmb_sub <- df_plot_filter %>%
    filter(Kids_First_Participant_ID == samples[i])
  
  # Define parameters for function
  ylim = max(df_plot_filter$mutation_count)
 
  # Run function
  p <- create_barplot_sample(tmb_df = tmb_sub,
                             ylim = ylim,
                             sid = samples[i],
                             palette = palette)
  print(p)
}
```

```{r echo=TRUE}
sessionInfo()
```
